Am´ eliorations futures

Parmi les aspects qui n’ont pas ´et´e ´etudi´es dans ce m´emoire, un des plus int´eressants serait sans doute d’´etablir des crit`eres permettant de discerner les ensembles de donn´ees pour les- quels il est pr´ef´erable d’estimer le vecteur al´eatoire de ceux pour lesquels il vaut mieux choisir les sc´enarios directement `a partir des donn´ees. Par exemple, quel intervalle de confiance sur

l’estimation des param`etres du vecteur al´eatoire est n´ecessaire afin que la QVAC soit avan- tageuse par rapport `a l’algorithme d’´echange des centres ? Un plus grand nombre de donn´ees avantage-t-il une m´ethode plus que l’autre ?

Il existe ´evidemment plusieurs m´ethodes de g´en´eration de sc´enarios. Nous n’en avons es- sentiellement consid´er´e qu’une seule dans ce m´emoire (avec certaines variantes). Il pourrait donc ˆetre int´eressant de confronter la quantification optimale `a d’autres m´ethodes de g´e- n´eration de sc´enarios. Existe-t-il des instances pour lesquelles certaines m´ethodes sont plus efficaces que d’autres ? Le cas ´ech´eant, est-il possible d’extraire les caract´eristiques du pro- bl`eme afin de s´electionner la m´ethode la plus performante ?

Il existe ´egalement une ´enorme quantit´e d’algorithmes des k-moyennes et k-m´edianes qui pourraient ˆetre utilis´es afin de minimiser la distance de Wasserstein et qui m´eritent d’ˆetre explor´es en fonction de la quantit´e de donn´ees disponibles, du nombre de sc´enarios requis et de la dimension du probl`eme.

Finalement, comme il ´et´e dit pr´ec´edemment, certaines des m´ethodes pr´esent´ees dans ce m´emoire n’ont pu ˆetre test´ees sur des applications concr`etes. Il serait donc n´ecessaire de les ´

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